{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\statsmodels\\tools\\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'D:\\\\Hackathon\\\\Machinehack\\\\E-Commerce_Participants_Data'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv('Train.csv')\n",
    "test=pd.read_csv('Test.csv')\n",
    "submission=pd.read_excel('Sample_Submission.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product</th>\n",
       "      <th>Product_Brand</th>\n",
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       "      <th>Subcategory_1</th>\n",
       "      <th>Subcategory_2</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P-2610</td>\n",
       "      <td>B-659</td>\n",
       "      <td>bags wallets belts</td>\n",
       "      <td>bags</td>\n",
       "      <td>hand bags</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2/3/2017</td>\n",
       "      <td>291.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P-2453</td>\n",
       "      <td>B-3078</td>\n",
       "      <td>clothing</td>\n",
       "      <td>women s clothing</td>\n",
       "      <td>western wear</td>\n",
       "      <td>3.1</td>\n",
       "      <td>7/1/2015</td>\n",
       "      <td>897.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P-6802</td>\n",
       "      <td>B-1810</td>\n",
       "      <td>home decor festive needs</td>\n",
       "      <td>showpieces</td>\n",
       "      <td>ethnic</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1/12/2019</td>\n",
       "      <td>792.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P-4452</td>\n",
       "      <td>B-3078</td>\n",
       "      <td>beauty and personal care</td>\n",
       "      <td>eye care</td>\n",
       "      <td>h2o plus eye care</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12/12/2014</td>\n",
       "      <td>837.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P-8454</td>\n",
       "      <td>B-3078</td>\n",
       "      <td>clothing</td>\n",
       "      <td>men s clothing</td>\n",
       "      <td>t shirts</td>\n",
       "      <td>4.3</td>\n",
       "      <td>12/12/2013</td>\n",
       "      <td>470.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product Product_Brand             Item_Category     Subcategory_1  \\\n",
       "0  P-2610         B-659        bags wallets belts              bags   \n",
       "1  P-2453        B-3078                  clothing  women s clothing   \n",
       "2  P-6802        B-1810  home decor festive needs        showpieces   \n",
       "3  P-4452        B-3078  beauty and personal care          eye care   \n",
       "4  P-8454        B-3078                  clothing    men s clothing   \n",
       "\n",
       "       Subcategory_2  Item_Rating        Date  Selling_Price  \n",
       "0          hand bags          4.3    2/3/2017          291.0  \n",
       "1       western wear          3.1    7/1/2015          897.0  \n",
       "2             ethnic          3.5   1/12/2019          792.0  \n",
       "3  h2o plus eye care          4.0  12/12/2014          837.0  \n",
       "4           t shirts          4.3  12/12/2013          470.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P-11284</td>\n",
       "      <td>B-2984</td>\n",
       "      <td>computers</td>\n",
       "      <td>network components</td>\n",
       "      <td>routers</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1/12/2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P-6580</td>\n",
       "      <td>B-1732</td>\n",
       "      <td>jewellery</td>\n",
       "      <td>bangles bracelets armlets</td>\n",
       "      <td>bracelets</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20/12/2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P-5843</td>\n",
       "      <td>B-3078</td>\n",
       "      <td>clothing</td>\n",
       "      <td>women s clothing</td>\n",
       "      <td>western wear</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1/12/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P-5334</td>\n",
       "      <td>B-1421</td>\n",
       "      <td>jewellery</td>\n",
       "      <td>necklaces chains</td>\n",
       "      <td>necklaces</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1/12/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P-5586</td>\n",
       "      <td>B-3078</td>\n",
       "      <td>clothing</td>\n",
       "      <td>women s clothing</td>\n",
       "      <td>western wear</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1/12/2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Product Product_Brand Item_Category              Subcategory_1  \\\n",
       "0  P-11284        B-2984     computers         network components   \n",
       "1   P-6580        B-1732     jewellery  bangles bracelets armlets   \n",
       "2   P-5843        B-3078      clothing           women s clothing   \n",
       "3   P-5334        B-1421     jewellery           necklaces chains   \n",
       "4   P-5586        B-3078      clothing           women s clothing   \n",
       "\n",
       "  Subcategory_2  Item_Rating        Date  \n",
       "0       routers          4.3   1/12/2018  \n",
       "1     bracelets          3.0  20/12/2012  \n",
       "2  western wear          1.5   1/12/2014  \n",
       "3     necklaces          3.9   1/12/2019  \n",
       "4  western wear          1.4   1/12/2017  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2921.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>508.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>802.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>402.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>751.10</td>\n",
       "    </tr>\n",
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       "   Selling_Price\n",
       "0        2921.95\n",
       "1         508.47\n",
       "2         802.71\n",
       "3         402.79\n",
       "4         751.10"
      ]
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x250ff677240>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['Selling_Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Target Variable is highly skewed. So we are going to normalize it by applying log transformation through np.log1p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Selling_Price']=np.log1p(train['Selling_Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x250ff9ee4a8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['Selling_Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Rating</th>\n",
       "      <th>Selling_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2452.000000</td>\n",
       "      <td>2452.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.078467</td>\n",
       "      <td>6.652575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.187137</td>\n",
       "      <td>1.177109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.526361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.918894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.100000</td>\n",
       "      <td>6.391917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.100000</td>\n",
       "      <td>7.086947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>11.663842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Item_Rating  Selling_Price\n",
       "count  2452.000000    2452.000000\n",
       "mean      3.078467       6.652575\n",
       "std       1.187137       1.177109\n",
       "min       1.000000       3.526361\n",
       "25%       2.000000       5.918894\n",
       "50%       3.100000       6.391917\n",
       "75%       4.100000       7.086947\n",
       "max       5.000000      11.663842"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2452 entries, 0 to 2451\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Product        2452 non-null   object \n",
      " 1   Product_Brand  2452 non-null   object \n",
      " 2   Item_Category  2452 non-null   object \n",
      " 3   Subcategory_1  2452 non-null   object \n",
      " 4   Subcategory_2  2452 non-null   object \n",
      " 5   Item_Rating    2452 non-null   float64\n",
      " 6   Date           2452 non-null   object \n",
      " 7   Selling_Price  2452 non-null   float64\n",
      "dtypes: float64(2), object(6)\n",
      "memory usage: 153.4+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product          2452\n",
       "Product_Brand    1010\n",
       "Item_Category      75\n",
       "Subcategory_1     130\n",
       "Subcategory_2     336\n",
       "Item_Rating        41\n",
       "Date              483\n",
       "Selling_Price    1095\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2452, 8)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[(train['Product_Brand']=='B-3078')]['Item_Category'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting Date features \n",
    "def extract_time_features(df):\n",
    "    df['Date']=pd.to_datetime(df['Date'])\n",
    "    #df['year'] = df['Date'].dt.year\n",
    "    df['month'] = df['Date'].dt.month\n",
    "    #df['day_of_week'] = df['Date'].dt.dayofweek\n",
    "    df['Day'] = df['Date'].dt.day\n",
    "    df['DayOfyear'] = df['Date'].dt.dayofyear\n",
    "    df['Week'] = pd.to_datetime(df['Date']).dt.week \n",
    "    df['Quarter'] = pd.to_datetime(df['Date']).dt.quarter  \n",
    "    df['Is_month_start'] = pd.to_datetime(df['Date']).dt.is_month_start \n",
    "    df['Is_month_end'] = pd.to_datetime(df['Date']).dt.is_month_end \n",
    "    del df['Date']\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=extract_time_features(train)\n",
    "test=extract_time_features(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Unique_Item_category_per_product_brand']=train.groupby(['Product_Brand'])['Item_Category'].transform('nunique')\n",
    "train['Unique_Subcategory_1_per_product_brand']=train.groupby(['Product_Brand'])['Subcategory_1'].transform('nunique')\n",
    "train['Unique_Subcategory_2_per_product_brand']=train.groupby(['Product_Brand'])['Subcategory_2'].transform('nunique')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Unique_Item_category_per_product_brand']=test.groupby(['Product_Brand'])['Item_Category'].transform('nunique')\n",
    "test['Unique_Subcategory_1_per_product_brand']=test.groupby(['Product_Brand'])['Subcategory_1'].transform('nunique')\n",
    "test['Unique_Subcategory_2_per_product_brand']=test.groupby(['Product_Brand'])['Subcategory_2'].transform('nunique')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product</th>\n",
       "      <th>Product_Brand</th>\n",
       "      <th>Item_Category</th>\n",
       "      <th>Subcategory_1</th>\n",
       "      <th>Subcategory_2</th>\n",
       "      <th>Item_Rating</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>month</th>\n",
       "      <th>Day</th>\n",
       "      <th>DayOfyear</th>\n",
       "      <th>Week</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Is_month_start</th>\n",
       "      <th>Is_month_end</th>\n",
       "      <th>Unique_Item_category_per_product_brand</th>\n",
       "      <th>Unique_Subcategory_1_per_product_brand</th>\n",
       "      <th>Unique_Subcategory_2_per_product_brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P-2610</td>\n",
       "      <td>B-659</td>\n",
       "      <td>bags wallets belts</td>\n",
       "      <td>bags</td>\n",
       "      <td>hand bags</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.676754</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P-2453</td>\n",
       "      <td>B-3078</td>\n",
       "      <td>clothing</td>\n",
       "      <td>women s clothing</td>\n",
       "      <td>western wear</td>\n",
       "      <td>3.1</td>\n",
       "      <td>6.800170</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>182</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P-6802</td>\n",
       "      <td>B-1810</td>\n",
       "      <td>home decor festive needs</td>\n",
       "      <td>showpieces</td>\n",
       "      <td>ethnic</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.675823</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P-4452</td>\n",
       "      <td>B-3078</td>\n",
       "      <td>beauty and personal care</td>\n",
       "      <td>eye care</td>\n",
       "      <td>h2o plus eye care</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.731018</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>346</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P-8454</td>\n",
       "      <td>B-3078</td>\n",
       "      <td>clothing</td>\n",
       "      <td>men s clothing</td>\n",
       "      <td>t shirts</td>\n",
       "      <td>4.3</td>\n",
       "      <td>6.154858</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>346</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product Product_Brand             Item_Category     Subcategory_1  \\\n",
       "0  P-2610         B-659        bags wallets belts              bags   \n",
       "1  P-2453        B-3078                  clothing  women s clothing   \n",
       "2  P-6802        B-1810  home decor festive needs        showpieces   \n",
       "3  P-4452        B-3078  beauty and personal care          eye care   \n",
       "4  P-8454        B-3078                  clothing    men s clothing   \n",
       "\n",
       "       Subcategory_2  Item_Rating  Selling_Price  month  Day  DayOfyear  Week  \\\n",
       "0          hand bags          4.3       5.676754      2    3         34     5   \n",
       "1       western wear          3.1       6.800170      7    1        182    27   \n",
       "2             ethnic          3.5       6.675823      1   12         12     2   \n",
       "3  h2o plus eye care          4.0       6.731018     12   12        346    50   \n",
       "4           t shirts          4.3       6.154858     12   12        346    50   \n",
       "\n",
       "   Quarter  Is_month_start  Is_month_end  \\\n",
       "0        1           False         False   \n",
       "1        3            True         False   \n",
       "2        1           False         False   \n",
       "3        4           False         False   \n",
       "4        4           False         False   \n",
       "\n",
       "   Unique_Item_category_per_product_brand  \\\n",
       "0                                       1   \n",
       "1                                      13   \n",
       "2                                       1   \n",
       "3                                      13   \n",
       "4                                      13   \n",
       "\n",
       "   Unique_Subcategory_1_per_product_brand  \\\n",
       "0                                       1   \n",
       "1                                      29   \n",
       "2                                       1   \n",
       "3                                      29   \n",
       "4                                      29   \n",
       "\n",
       "   Unique_Subcategory_2_per_product_brand  \n",
       "0                                       1  \n",
       "1                                     100  \n",
       "2                                       1  \n",
       "3                                     100  \n",
       "4                                     100  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will concat both train and test data set to apply some feature engineering like Label Encoder\n",
    "total=pd.concat([train,test],0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product</th>\n",
       "      <th>Product_Brand</th>\n",
       "      <th>Item_Category</th>\n",
       "      <th>Subcategory_1</th>\n",
       "      <th>Subcategory_2</th>\n",
       "      <th>Item_Rating</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>month</th>\n",
       "      <th>Day</th>\n",
       "      <th>DayOfyear</th>\n",
       "      <th>Week</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Is_month_start</th>\n",
       "      <th>Is_month_end</th>\n",
       "      <th>Unique_Item_category_per_product_brand</th>\n",
       "      <th>Unique_Subcategory_1_per_product_brand</th>\n",
       "      <th>Unique_Subcategory_2_per_product_brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P-2610</td>\n",
       "      <td>B-659</td>\n",
       "      <td>bags wallets belts</td>\n",
       "      <td>bags</td>\n",
       "      <td>hand bags</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.676754</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P-2453</td>\n",
       "      <td>B-3078</td>\n",
       "      <td>clothing</td>\n",
       "      <td>women s clothing</td>\n",
       "      <td>western wear</td>\n",
       "      <td>3.1</td>\n",
       "      <td>6.800170</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>182</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P-6802</td>\n",
       "      <td>B-1810</td>\n",
       "      <td>home decor festive needs</td>\n",
       "      <td>showpieces</td>\n",
       "      <td>ethnic</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.675823</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P-4452</td>\n",
       "      <td>B-3078</td>\n",
       "      <td>beauty and personal care</td>\n",
       "      <td>eye care</td>\n",
       "      <td>h2o plus eye care</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.731018</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>346</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P-8454</td>\n",
       "      <td>B-3078</td>\n",
       "      <td>clothing</td>\n",
       "      <td>men s clothing</td>\n",
       "      <td>t shirts</td>\n",
       "      <td>4.3</td>\n",
       "      <td>6.154858</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>346</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product Product_Brand             Item_Category     Subcategory_1  \\\n",
       "0  P-2610         B-659        bags wallets belts              bags   \n",
       "1  P-2453        B-3078                  clothing  women s clothing   \n",
       "2  P-6802        B-1810  home decor festive needs        showpieces   \n",
       "3  P-4452        B-3078  beauty and personal care          eye care   \n",
       "4  P-8454        B-3078                  clothing    men s clothing   \n",
       "\n",
       "       Subcategory_2  Item_Rating  Selling_Price  month  Day  DayOfyear  Week  \\\n",
       "0          hand bags          4.3       5.676754      2    3         34     5   \n",
       "1       western wear          3.1       6.800170      7    1        182    27   \n",
       "2             ethnic          3.5       6.675823      1   12         12     2   \n",
       "3  h2o plus eye care          4.0       6.731018     12   12        346    50   \n",
       "4           t shirts          4.3       6.154858     12   12        346    50   \n",
       "\n",
       "   Quarter  Is_month_start  Is_month_end  \\\n",
       "0        1           False         False   \n",
       "1        3            True         False   \n",
       "2        1           False         False   \n",
       "3        4           False         False   \n",
       "4        4           False         False   \n",
       "\n",
       "   Unique_Item_category_per_product_brand  \\\n",
       "0                                       1   \n",
       "1                                      13   \n",
       "2                                       1   \n",
       "3                                      13   \n",
       "4                                      13   \n",
       "\n",
       "   Unique_Subcategory_1_per_product_brand  \\\n",
       "0                                       1   \n",
       "1                                      29   \n",
       "2                                       1   \n",
       "3                                      29   \n",
       "4                                      29   \n",
       "\n",
       "   Unique_Subcategory_2_per_product_brand  \n",
       "0                                       1  \n",
       "1                                     100  \n",
       "2                                       1  \n",
       "3                                     100  \n",
       "4                                     100  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# total['average_rating_per_product_brand']=total.groupby(['Product_Brand'])['Item_Rating'].transform('mean')\n",
    "# total['average_rating_Item_Category']=total.groupby(['Item_Category'])['Item_Rating'].transform('mean')\n",
    "# total['average_rating_Subcategory_1']=total.groupby(['Subcategory_1'])['Item_Rating'].transform('mean')\n",
    "# total['average_rating_Subcategory_2']=total.groupby(['Subcategory_2'])['Item_Rating'].transform('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "total['std_rating_per_product_brand']=total.groupby(['Product_Brand'])['Item_Rating'].transform('std')\n",
    "total['std_rating_Item_Category']=total.groupby(['Item_Category'])['Item_Rating'].transform('std')\n",
    "total['std_rating_Subcategory_1']=total.groupby(['Subcategory_1'])['Item_Rating'].transform('std')\n",
    "total['std_rating_Subcategory_2']=total.groupby(['Subcategory_2'])['Item_Rating'].transform('std')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# total['var_rating_per_product_brand']=total.groupby(['Product_Brand'])['Item_Rating'].transform('var')\n",
    "# total['var_rating_Item_Category']=total.groupby(['Item_Category'])['Item_Rating'].transform('var')\n",
    "# total['var_rating_Subcategory_1']=total.groupby(['Subcategory_1'])['Item_Rating'].transform('var')\n",
    "# total['var_rating_Subcategory_2']=total.groupby(['Subcategory_2'])['Item_Rating'].transform('var')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "total['Product_Brand']=total['Product_Brand'].str.lstrip('B-').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat=['Item_Category','Subcategory_1','Subcategory_2']\n",
    "for items in cat:\n",
    "    total[items]=le.fit_transform(total[items])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product</th>\n",
       "      <th>Product_Brand</th>\n",
       "      <th>Item_Category</th>\n",
       "      <th>Subcategory_1</th>\n",
       "      <th>Subcategory_2</th>\n",
       "      <th>Item_Rating</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>month</th>\n",
       "      <th>Day</th>\n",
       "      <th>DayOfyear</th>\n",
       "      <th>...</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Is_month_start</th>\n",
       "      <th>Is_month_end</th>\n",
       "      <th>Unique_Item_category_per_product_brand</th>\n",
       "      <th>Unique_Subcategory_1_per_product_brand</th>\n",
       "      <th>Unique_Subcategory_2_per_product_brand</th>\n",
       "      <th>std_rating_per_product_brand</th>\n",
       "      <th>std_rating_Item_Category</th>\n",
       "      <th>std_rating_Subcategory_1</th>\n",
       "      <th>std_rating_Subcategory_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P-2610</td>\n",
       "      <td>659</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>159</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.676754</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.105474</td>\n",
       "      <td>1.094790</td>\n",
       "      <td>1.018614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P-2453</td>\n",
       "      <td>3078</td>\n",
       "      <td>17</td>\n",
       "      <td>139</td>\n",
       "      <td>387</td>\n",
       "      <td>3.1</td>\n",
       "      <td>6.800170</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>182</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>100</td>\n",
       "      <td>1.197104</td>\n",
       "      <td>1.205383</td>\n",
       "      <td>1.227087</td>\n",
       "      <td>1.186726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P-6802</td>\n",
       "      <td>1810</td>\n",
       "      <td>38</td>\n",
       "      <td>119</td>\n",
       "      <td>118</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.675823</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.202082</td>\n",
       "      <td>1.192523</td>\n",
       "      <td>1.094301</td>\n",
       "      <td>1.028398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P-4452</td>\n",
       "      <td>3078</td>\n",
       "      <td>12</td>\n",
       "      <td>40</td>\n",
       "      <td>155</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.731018</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>346</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>100</td>\n",
       "      <td>1.197104</td>\n",
       "      <td>1.215595</td>\n",
       "      <td>0.636396</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P-8454</td>\n",
       "      <td>3078</td>\n",
       "      <td>17</td>\n",
       "      <td>86</td>\n",
       "      <td>344</td>\n",
       "      <td>4.3</td>\n",
       "      <td>6.154858</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>346</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>100</td>\n",
       "      <td>1.197104</td>\n",
       "      <td>1.205383</td>\n",
       "      <td>1.146774</td>\n",
       "      <td>1.123001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product  Product_Brand  Item_Category  Subcategory_1  Subcategory_2  \\\n",
       "0  P-2610            659              9             11            159   \n",
       "1  P-2453           3078             17            139            387   \n",
       "2  P-6802           1810             38            119            118   \n",
       "3  P-4452           3078             12             40            155   \n",
       "4  P-8454           3078             17             86            344   \n",
       "\n",
       "   Item_Rating  Selling_Price  month  Day  DayOfyear  ...  Quarter  \\\n",
       "0          4.3       5.676754      2    3         34  ...        1   \n",
       "1          3.1       6.800170      7    1        182  ...        3   \n",
       "2          3.5       6.675823      1   12         12  ...        1   \n",
       "3          4.0       6.731018     12   12        346  ...        4   \n",
       "4          4.3       6.154858     12   12        346  ...        4   \n",
       "\n",
       "   Is_month_start  Is_month_end  Unique_Item_category_per_product_brand  \\\n",
       "0           False         False                                       1   \n",
       "1            True         False                                      13   \n",
       "2           False         False                                       1   \n",
       "3           False         False                                      13   \n",
       "4           False         False                                      13   \n",
       "\n",
       "   Unique_Subcategory_1_per_product_brand  \\\n",
       "0                                       1   \n",
       "1                                      29   \n",
       "2                                       1   \n",
       "3                                      29   \n",
       "4                                      29   \n",
       "\n",
       "   Unique_Subcategory_2_per_product_brand  std_rating_per_product_brand  \\\n",
       "0                                       1                           NaN   \n",
       "1                                     100                      1.197104   \n",
       "2                                       1                      1.202082   \n",
       "3                                     100                      1.197104   \n",
       "4                                     100                      1.197104   \n",
       "\n",
       "   std_rating_Item_Category  std_rating_Subcategory_1  \\\n",
       "0                  1.105474                  1.094790   \n",
       "1                  1.205383                  1.227087   \n",
       "2                  1.192523                  1.094301   \n",
       "3                  1.215595                  0.636396   \n",
       "4                  1.205383                  1.146774   \n",
       "\n",
       "   std_rating_Subcategory_2  \n",
       "0                  1.018614  \n",
       "1                  1.186726  \n",
       "2                  1.028398  \n",
       "3                       NaN  \n",
       "4                  1.123001  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2452, 17)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_final=total[:train.shape[0]]\n",
    "test_final=total[train.shape[0]:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2452, 21)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1051, 21)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=train_final.drop(['Product','Selling_Price'],1)\n",
    "y=train_final['Selling_Price']\n",
    "test_final=test_final.drop(['Product','Selling_Price'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:28:42] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[0]\tvalidation_0-rmse:6.1829\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[100]\tvalidation_0-rmse:2.39335\n",
      "[200]\tvalidation_0-rmse:1.10566\n",
      "[300]\tvalidation_0-rmse:0.748487\n",
      "[400]\tvalidation_0-rmse:0.670934\n",
      "[500]\tvalidation_0-rmse:0.653117\n",
      "[600]\tvalidation_0-rmse:0.646693\n",
      "[700]\tvalidation_0-rmse:0.643097\n",
      "[800]\tvalidation_0-rmse:0.639965\n",
      "[900]\tvalidation_0-rmse:0.638277\n",
      "[1000]\tvalidation_0-rmse:0.636763\n",
      "[1100]\tvalidation_0-rmse:0.636547\n",
      "[1200]\tvalidation_0-rmse:0.636175\n",
      "[1300]\tvalidation_0-rmse:0.636223\n",
      "Stopping. Best iteration:\n",
      "[1286]\tvalidation_0-rmse:0.636041\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=0.8, eval_metric='rmse',\n",
       "             gamma=0, importance_type='gain', learning_rate=0.01,\n",
       "             max_delta_step=0, max_depth=6, min_child_weight=1, missing=None,\n",
       "             n_estimators=5000, n_jobs=1, nthread=None, objective='reg:linear',\n",
       "             random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "             seed=100, silent=None, subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "model_2 = XGBRegressor(\n",
    " learning_rate =0.01,\n",
    " n_estimators=5000,\n",
    " max_depth=6,\n",
    " colsample_bytree=0.8,\n",
    " seed=100,\n",
    " eval_metric='rmse'\n",
    " )\n",
    "#model.fit(X_train, y_train)\n",
    "model_2.fit(X_train, y_train, eval_metric='rmse', \n",
    "          eval_set=[(X_test, y_test)], early_stopping_rounds=100, verbose=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:28:46] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "xgb=XGBRegressor(\n",
    " learning_rate =0.01,\n",
    " n_estimators=1350,\n",
    " max_depth=6,\n",
    " colsample_bytree=0.8,\n",
    " seed=100)\n",
    "xgb_model=xgb.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:28:49] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[12:28:53] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[12:28:56] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[12:28:59] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[12:29:02] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "score=cross_val_score(X=X,y=y,estimator=xgb,scoring='neg_root_mean_squared_error',cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.70489114, -0.63219413, -0.64454623, -0.64454668, -0.66270616])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6577768670309349"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1=xgb.predict(test_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1=np.expm1(y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2317.383  ,  395.8467 ,  743.4759 , ..., 1353.5061 ,  383.00787,\n",
       "       1349.5165 ], dtype=float32)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Product_Brand</td>\n",
       "      <td>1.417697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Item_Category</td>\n",
       "      <td>3.897341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Subcategory_1</td>\n",
       "      <td>3.069462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Subcategory_2</td>\n",
       "      <td>7.511923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Item_Rating</td>\n",
       "      <td>0.827977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>month</td>\n",
       "      <td>2.562474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Day</td>\n",
       "      <td>3.090106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DayOfyear</td>\n",
       "      <td>25.312138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Week</td>\n",
       "      <td>1.465710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Quarter</td>\n",
       "      <td>2.866860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Is_month_start</td>\n",
       "      <td>10.438597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Is_month_end</td>\n",
       "      <td>1.604497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Unique_Item_category_per_product_brand</td>\n",
       "      <td>2.393835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Unique_Subcategory_1_per_product_brand</td>\n",
       "      <td>3.087416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Unique_Subcategory_2_per_product_brand</td>\n",
       "      <td>1.429410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>std_rating_per_product_brand</td>\n",
       "      <td>1.855305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>std_rating_Item_Category</td>\n",
       "      <td>7.486279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>std_rating_Subcategory_1</td>\n",
       "      <td>16.457479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>std_rating_Subcategory_2</td>\n",
       "      <td>3.225492</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         0          1\n",
       "0                            Product_Brand   1.417697\n",
       "1                            Item_Category   3.897341\n",
       "2                            Subcategory_1   3.069462\n",
       "3                            Subcategory_2   7.511923\n",
       "4                              Item_Rating   0.827977\n",
       "5                                    month   2.562474\n",
       "6                                      Day   3.090106\n",
       "7                                DayOfyear  25.312138\n",
       "8                                     Week   1.465710\n",
       "9                                  Quarter   2.866860\n",
       "10                          Is_month_start  10.438597\n",
       "11                            Is_month_end   1.604497\n",
       "12  Unique_Item_category_per_product_brand   2.393835\n",
       "13  Unique_Subcategory_1_per_product_brand   3.087416\n",
       "14  Unique_Subcategory_2_per_product_brand   1.429410\n",
       "15            std_rating_per_product_brand   1.855305\n",
       "16                std_rating_Item_Category   7.486279\n",
       "17                std_rating_Subcategory_1  16.457479\n",
       "18                std_rating_Subcategory_2   3.225492"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(list(zip(X.columns,xgb_model.feature_importances_*100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boosting_type': 'gbdt',\n",
       " 'objective': 'regression',\n",
       " 'metric': 'rmse',\n",
       " 'verbose': 0,\n",
       " 'bagging_fraction': 0.8,\n",
       " 'bagging_freq': 1,\n",
       " 'lambda_l1': 0.01,\n",
       " 'lambda_l2': 0.01,\n",
       " 'learning_rate': 0.01,\n",
       " 'max_bin': 255,\n",
       " 'max_depth': 6,\n",
       " 'min_data_in_bin': 1,\n",
       " 'min_data_in_leaf': 1,\n",
       " 'num_leaves': 31}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lightgbm import LGBMRegressor\n",
    "lgb_fit_params={\"early_stopping_rounds\":50, \n",
    "            \"eval_metric\" : 'rmse', \n",
    "            \"eval_set\" : [(X_test,y_test)],\n",
    "            'eval_names': ['valid'],\n",
    "            'verbose':100\n",
    "           }\n",
    "\n",
    "lgb_params = {'boosting_type': 'gbdt',\n",
    " 'objective': 'regression',\n",
    " 'metric': 'rmse',\n",
    " 'verbose': 0,\n",
    " 'bagging_fraction': 0.8,\n",
    " 'bagging_freq': 1,\n",
    " 'lambda_l1': 0.01,\n",
    " 'lambda_l2': 0.01,\n",
    " 'learning_rate': 0.01,\n",
    " 'max_bin': 255,\n",
    " 'max_depth': 6,\n",
    " 'min_data_in_bin': 1,\n",
    " 'min_data_in_leaf': 1,\n",
    " 'num_leaves': 31}\n",
    "lgb_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 50 rounds\n",
      "[100]\tvalid's rmse: 0.771544\n",
      "[200]\tvalid's rmse: 0.683172\n",
      "[300]\tvalid's rmse: 0.655636\n",
      "[400]\tvalid's rmse: 0.644102\n",
      "[500]\tvalid's rmse: 0.639337\n",
      "[600]\tvalid's rmse: 0.63667\n",
      "[700]\tvalid's rmse: 0.634326\n",
      "[800]\tvalid's rmse: 0.633185\n",
      "[900]\tvalid's rmse: 0.632294\n",
      "Early stopping, best iteration is:\n",
      "[940]\tvalid's rmse: 0.631924\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "940"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_lgb = LGBMRegressor(n_estimators=10000, **lgb_params, random_state=123456789, n_jobs=-1)\n",
    "clf_lgb.fit(X_train, y_train, **lgb_fit_params)\n",
    "clf_lgb.best_iteration_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_lgb=LGBMRegressor(n_estimators=int(clf_lgb.best_iteration_*1.2), **lgb_params)\n",
    "lgb_model=clf_lgb.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "score=cross_val_score(X=X,y=y,estimator=clf_lgb,scoring='neg_root_mean_squared_error',cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.70730587, -0.65582149, -0.64233212, -0.64650094, -0.66590654])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6635733910463217"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Product_Brand</td>\n",
       "      <td>5242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Item_Category</td>\n",
       "      <td>1922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Subcategory_1</td>\n",
       "      <td>1931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Subcategory_2</td>\n",
       "      <td>3046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Item_Rating</td>\n",
       "      <td>3713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>month</td>\n",
       "      <td>931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Day</td>\n",
       "      <td>1697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DayOfyear</td>\n",
       "      <td>1778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Week</td>\n",
       "      <td>1169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Quarter</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Is_month_start</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Is_month_end</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Unique_Item_category_per_product_brand</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Unique_Subcategory_1_per_product_brand</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Unique_Subcategory_2_per_product_brand</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>std_rating_per_product_brand</td>\n",
       "      <td>3046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>std_rating_Item_Category</td>\n",
       "      <td>1802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>std_rating_Subcategory_1</td>\n",
       "      <td>2501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>std_rating_Subcategory_2</td>\n",
       "      <td>3550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         0     1\n",
       "0                            Product_Brand  5242\n",
       "1                            Item_Category  1922\n",
       "2                            Subcategory_1  1931\n",
       "3                            Subcategory_2  3046\n",
       "4                              Item_Rating  3713\n",
       "5                                    month   931\n",
       "6                                      Day  1697\n",
       "7                                DayOfyear  1778\n",
       "8                                     Week  1169\n",
       "9                                  Quarter     8\n",
       "10                          Is_month_start    42\n",
       "11                            Is_month_end    19\n",
       "12  Unique_Item_category_per_product_brand   159\n",
       "13  Unique_Subcategory_1_per_product_brand   115\n",
       "14  Unique_Subcategory_2_per_product_brand   151\n",
       "15            std_rating_per_product_brand  3046\n",
       "16                std_rating_Item_Category  1802\n",
       "17                std_rating_Subcategory_1  2501\n",
       "18                std_rating_Subcategory_2  3550"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(list(zip(X.columns,clf_lgb.feature_importances_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred2=lgb_model.predict(test_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred2=np.expm1(y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2216.00499484,  378.73163792,  755.94135457, ..., 1365.44078059,\n",
       "        396.66753436, 1358.70092729])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CatBoost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x250ffe89c88>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from catboost import CatBoostRegressor\n",
    "cbr=CatBoostRegressor()\n",
    "cbr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "999:\tlearn: 0.4041397\ttotal: 1.41s\tremaining: 0us\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "score=cross_val_score(X=X,y=y,estimator=cbr,scoring='neg_root_mean_squared_error',cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.71430941, -0.62946454, -0.65309555, -0.66681265, -0.65475282])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6636869935067751"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2176.27436856,  394.57362385,  799.75802591, ..., 1342.73455371,\n",
       "        526.69600309, 1342.83182061])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred3=cbr.predict(test_final)\n",
    "\n",
    "y_pred3=np.expm1(y_pred3)\n",
    "\n",
    "y_pred3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2222.1697445 ,  382.02734374,  759.07647383, ..., 1361.9766963 ,\n",
       "        408.30441668, 1356.19556879])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=(0.1*y_pred1)+(y_pred2*0.8)+(y_pred3*0.1)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_new=pd.DataFrame(np.round(abs(y_pred),2),columns=['Selling_Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2222.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>382.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>759.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>355.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>749.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price\n",
       "0        2222.17\n",
       "1         382.03\n",
       "2         759.08\n",
       "3         355.33\n",
       "4         749.95"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_new.to_excel('submission_final.xlsx',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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